Thermoregulation model JOS-3 with new open source code

Takahashi, Yoshito; Nomoto, Akihisa; Yoda, Shu; Hisayama, Ryo; Ogata, Masayuki; Ozeki, Yoshiichi; Tanabe, Shin Ichi

doi:10.1016/j.enbuild.2020.110575

Cited by 62 publications

(26 citation statements)

References 33 publications

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“…In this paper, a multi-segment human bioheat model proposed by Tanabe et al [43] was used to simulate the thermophysiological responses of grid workers under different hot environments. Based on the previous studies [44,45], the model named JOS-3 newly introduced the effects of brown adipose tissue activity, age and solar radiation (including long-wave radiation and short-wave radiation).…”

Section: Human Bioheat Modelmentioning

confidence: 99%

“…where Err i,j is the error signals of node (i, j), • C; T i,j and Tsetpt i,j are the temperature of setpoint temperature of node (i, j), respectively, • C. The detailed information of the thermoregulation process equations can be acquired in [43].The body temperature will maintain stability when there is a heat balance between the human body and its external environment [23], that is, the heat generated and stored by the human body is equal to the heat lost to the external environment. The heat balance equations are used to describe the dynamic heat exchange process.…”

Section: Human Bioheat Modelmentioning

confidence: 99%

See 1 more Smart Citation

Heat Strain Evaluation of Power Grid Outdoor Workers Based on a Human Bioheat Model

Sun

et al. 2022

IJERPH

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Power grid outdoor workers are usually exposed to hot environments and could suffer the threats to occupational health and safety like heat strain and injury. In order to predict and assess the thermophysiological responses of grid workers in the heat, the clothing thermal insulation of grid worker ensembles was measured by a thermal manikin and a multi-segment human bioheat model was employed to evaluate the thermophysiological response parameters of grid workers such as core temperature, skin temperature and sweat loss. The results show that working in a hot environment can cause a obvious increase in core temperature and skin temperature of grid workers, and the acceptable maximum working time of grid workers varies greatly in different hot environments. A reasonable work organization strategy can effectively decrease the core temperature and sweat loss, increasing the duration of acceptable maximum working time for grid workers. This study is helpful to assess heat-related risks of grid workers and support power grid companies to rationalize work organization strategies and personal protection guidelines.

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Section: Human Bioheat Modelmentioning

confidence: 99%

Section: Human Bioheat Modelmentioning

confidence: 99%

Heat Strain Evaluation of Power Grid Outdoor Workers Based on a Human Bioheat Model

Sun

et al. 2022

IJERPH

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“…Thermal sensation is the consequence of complex interaction between thermoregulation and cognition. Takahashi et al [ 19 ] indicate thermoregulation includes several physiological activities, such as vasodilation, vasoconstriction, shivering, and sweating; hence, the related physiological signals could possibly be the evidence of TS.…”

Section: Introductionmentioning

confidence: 99%

A Physiological-Signal-Based Thermal Sensation Model for Indoor Environment Thermal Comfort Evaluation

Pao

Liang

et al. 2022

IJERPH

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Traditional heating, ventilation, and air conditioning (HVAC) control systems rely mostly on static models, such as Fanger’s predicted mean vote (PMV) to predict human thermal comfort in indoor environments. Such models consider environmental parameters, such as room temperature, humidity, etc., and indirect human factors, such as metabolic rate, clothing, etc., which do not necessarily reflect the actual human thermal comfort. Therefore, as electronic sensor devices have become widely used, we propose to develop a thermal sensation (TS) model that takes in humans’ physiological signals for consideration in addition to the environment parameters. We conduct climate chamber experiments to collect physiological signals and personal TS under different environments. The collected physiological signals are ECG, EEG, EMG, GSR, and body temperatures. As a preliminary study, we conducted experiments on young subjects under static behaviors by controlling the room temperature, fan speed, and humidity. The results show that our physiological-signal-based TS model performs much better than the PMV model, with average RMSEs 0.75 vs. 1.07 (lower is better) and R2 0.77 vs. 0.43 (higher is better), respectively, meaning that our model prediction has higher accuracy and better explainability. The experiments also ranked the importance of physiological signals (as EMG, body temperature, ECG, and EEG, in descending order) so they can be selectively adopted according to the feasibility of signal collection in different application scenarios. This study demonstrates the usefulness of physiological signals in TS prediction and motivates further thorough research on wider scenarios, such as ages, health condition, static/motion/sports behaviors, etc.

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“…Clothing parameters for body regions can be measured, and the change due to walking and wind can be estimated [ 35 , 36 , 37 , 38 ]. Local values can be used in advanced physiological models for exposure evaluation [ 26 , 39 , 40 , 41 , 42 , 43 , 44 ], for occupational health and safety purposes, as a feedback for clothing manufacturers for improving their products, and/or for industries for selecting the clothing provided to their employees. In addition, some publications provide information on how to count for fit, garment size and/or airgaps [ 37 , 43 , 45 , 46 ].…”

Section: Introductionmentioning

confidence: 99%

A Database of Static Thermal Insulation and Evaporative Resistance Values of Dutch Firefighter Clothing Items and Ensembles

Kuklane¹,

Eggeling

KEMMEREN

et al. 2022

Biology

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The rescue operations’ environment can impair firefighters’ performance and increase the risk of injuries, e.g., burns and hyperthermia. The bulk and carried weight of heavy protection contributes to lower physical performance, higher metabolic load and internal body heat production. For recommending optimal protection for the tasks and incident scenarios, knowledge of clothing thermal properties is needed. However, detailed data on firefighter protective clothing systems are not available. The aim of the study was to provide scientific background and a dataset that would allow for validation of thermo-physiological models for task-specific conditions of rescue work. Thermal insulation of 37 single items and their variations and 25 realistic protective clothing ensembles were measured on a thermal manikin. Twelve (12) ensembles that evenly covered the whole insulation range were selected for evaporative resistance testing. The equations for summing up individual item’s insulation to ensemble insulation and calculating clothing area factor were derived from the dataset. The database of a firefighter clothing system was created. In addition, the local and regional thermal properties of the clothing ensembles were provided for use in future validation of advanced thermo-physiological models for rescue worker exposure predictions and for designing decision aid tools.

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Thermoregulation model JOS-3 with new open source code

Cited by 62 publications

References 33 publications

Heat Strain Evaluation of Power Grid Outdoor Workers Based on a Human Bioheat Model

Heat Strain Evaluation of Power Grid Outdoor Workers Based on a Human Bioheat Model

A Physiological-Signal-Based Thermal Sensation Model for Indoor Environment Thermal Comfort Evaluation

A Database of Static Thermal Insulation and Evaporative Resistance Values of Dutch Firefighter Clothing Items and Ensembles

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